
@inproceedings{dalal_histograms_2005,
	title = {Histograms of oriented gradients for human detection},
	volume = {1},
	booktitle = {Computer {Vision} and {Pattern} {Recognition}, 2005. {CVPR} 2005. {IEEE} {Computer} {Society} {Conference} on},
	publisher = {IEEE},
	author = {Dalal, Navneet and Triggs, Bill},
	year = {2005},
	pages = {886--893}
}

@article{he_mask_2017,
	title = {Mask {R}-{CNN}},
	volume = {abs/1703.06870},
	url = {http://arxiv.org/abs/1703.06870},
	journal = {CoRR},
	author = {He, Kaiming and Gkioxari, Georgia and Dollár, Piotr and Girshick, Ross B.},
	year = {2017}
}

@article{girshick_fast_2015,
	title = {Fast {R}-{CNN}},
	volume = {abs/1504.08083},
	url = {http://arxiv.org/abs/1504.08083},
	journal = {CoRR},
	author = {Girshick, Ross B.},
	year = {2015}
}

@article{dai_r-fcn:_2016,
	title = {R-{FCN}: {Object} {Detection} via {Region}-based {Fully} {Convolutional} {Networks}},
	volume = {abs/1605.06409},
	url = {http://arxiv.org/abs/1605.06409},
	journal = {CoRR},
	author = {Dai, Jifeng and Li, Yi and He, Kaiming and Sun, Jian},
	year = {2016}
}

@article{he_spatial_2014,
	title = {Spatial {Pyramid} {Pooling} in {Deep} {Convolutional} {Networks} for {Visual} {Recognition}},
	volume = {abs/1406.4729},
	url = {http://arxiv.org/abs/1406.4729},
	journal = {CoRR},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	year = {2014}
}

@inproceedings{long_fully_2015,
	title = {Fully convolutional networks for semantic segmentation},
	booktitle = {Proceedings of the {IEEE} conference on computer vision and pattern recognition},
	author = {Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
	year = {2015},
	pages = {3431--3440}
}

@article{girshick_rich_2013,
	title = {Rich feature hierarchies for accurate object detection and semantic segmentation},
	volume = {abs/1311.2524},
	url = {http://arxiv.org/abs/1311.2524},
	journal = {CoRR},
	author = {Girshick, Ross B. and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
	year = {2013}
}

@inproceedings{zeiler_visualizing_2014,
	title = {Visualizing and understanding convolutional networks},
	booktitle = {European conference on computer vision},
	publisher = {Springer},
	author = {Zeiler, Matthew D and Fergus, Rob},
	year = {2014},
	pages = {818--833}
}

@article{uijlings_selective_2013,
	title = {Selective {Search} for {Object} {Recognition}},
	volume = {104},
	number = {2},
	journal = {International Journal of Computer Vision},
	author = {Uijlings, J. R. and Sande, K. E. and Gevers, T. and Smeulders, A. W.},
	year = {2013},
	pages = {154--171}
}

@misc{noauthor__nodate,
	title = {条件数 - 维基百科，自由的百科全书},
	url = {https://zh.wikipedia.org/wiki/%E6%9D%A1%E4%BB%B6%E6%95%B0},
	urldate = {2018-11-13}
}

@inproceedings{kazemi_one_2014,
	address = {Columbus, OH},
	title = {One millisecond face alignment with an ensemble of regression trees},
	isbn = {978-1-4799-5118-5},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6909637},
	doi = {10.1109/CVPR.2014.241},
	abstract = {This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face’s landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efﬁcient feature selection. Different regularization strategies and its importance to combat overﬁtting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.},
	language = {en},
	urldate = {2018-12-05},
	booktitle = {2014 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	publisher = {IEEE},
	author = {Kazemi, Vahid and Sullivan, Josephine},
	month = jun,
	year = {2014},
	pages = {1867--1874},
	file = {Kazemi 和 Sullivan - 2014 - One millisecond face alignment with an ensemble of.pdf:/home/liushuai/Zotero/storage/UYYJDJ5Y/Kazemi 和 Sullivan - 2014 - One millisecond face alignment with an ensemble of.pdf:application/pdf}
}

@inproceedings{redmon_you_2016,
	address = {Las Vegas, NV, USA},
	title = {You {Only} {Look} {Once}: {Unified}, {Real}-{Time} {Object} {Detection}},
	isbn = {978-1-4673-8851-1},
	shorttitle = {You {Only} {Look} {Once}},
	url = {http://ieeexplore.ieee.org/document/7780460/},
	doi = {10.1109/CVPR.2016.91},
	abstract = {We present YOLO, a new approach to object detection. Prior work on object detection repurposes classiﬁers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.},
	language = {en},
	urldate = {2018-12-05},
	booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},
	month = jun,
	year = {2016},
	pages = {779--788},
	file = {Redmon 等。 - 2016 - You Only Look Once Unified, Real-Time Object Dete.pdf:/home/liushuai/Zotero/storage/6VKA2I49/Redmon 等。 - 2016 - You Only Look Once Unified, Real-Time Object Dete.pdf:application/pdf}
}

@article{ren_faster_2015,
	title = {Faster {R}-{CNN}: {Towards} {Real}-{Time} {Object} {Detection} with {Region} {Proposal} {Networks}},
	shorttitle = {Faster {R}-{CNN}},
	url = {http://arxiv.org/abs/1506.01497},
	abstract = {State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with “attention” mechanisms, the RPN component tells the uniﬁed network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.},
	language = {en},
	urldate = {2018-12-05},
	journal = {arXiv:1506.01497 [cs]},
	author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
	month = jun,
	year = {2015},
	note = {arXiv: 1506.01497},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Extended tech report},
	file = {Ren 等。 - 2015 - Faster R-CNN Towards Real-Time Object Detection w.pdf:/home/liushuai/Zotero/storage/GBIRWGMI/Ren 等。 - 2015 - Faster R-CNN Towards Real-Time Object Detection w.pdf:application/pdf}
}

@article{ren_faster_2015-1,
	title = {Faster {R}-{CNN}: {Towards} {Real}-{Time} {Object} {Detection} with {Region} {Proposal} {Networks}},
	shorttitle = {Faster {R}-{CNN}},
	url = {http://arxiv.org/abs/1506.01497},
	abstract = {State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with “attention” mechanisms, the RPN component tells the uniﬁed network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.},
	language = {en},
	urldate = {2018-12-05},
	journal = {arXiv:1506.01497 [cs]},
	author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
	month = jun,
	year = {2015},
	note = {arXiv: 1506.01497},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Extended tech report},
	file = {Ren 等。 - 2015 - Faster R-CNN Towards Real-Time Object Detection w.pdf:/home/liushuai/Zotero/storage/2MQDNR4V/Ren 等。 - 2015 - Faster R-CNN Towards Real-Time Object Detection w.pdf:application/pdf}
}

@article{_linuxshell3_nodate,
	title = {Linux命令行与shell脚本编程大全（第3版）},
	language = {zh},
	author = {书涵盖了详尽的动手教程和实践信息, 本},
	pages = {623},
	file = {书涵盖了详尽的动手教程和实践信息 - Linux命令行与shell脚本编程大全（第3版）.pdf:/home/liushuai/Zotero/storage/E8C59WBN/书涵盖了详尽的动手教程和实践信息 - Linux命令行与shell脚本编程大全（第3版）.pdf:application/pdf}
}

@book{summerfield_rapid_2008,
	address = {Upper Saddle River, NJ},
	series = {Prentice {Hall} open source software development series},
	title = {Rapid {GUI} programming with {Python} and {Qt}: the definitive guide to {PyQt} programming},
	isbn = {978-0-13-235418-9},
	shorttitle = {Rapid {GUI} programming with {Python} and {Qt}},
	language = {en},
	publisher = {Prentice Hall},
	author = {Summerfield, Mark},
	year = {2008},
	note = {OCLC: ocn166273850},
	keywords = {Graphical user interfaces (Computer systems), Python (Computer program language), Qt (Electronic resource)},
	file = {Summerfield - 2008 - Rapid GUI programming with Python and Qt the defi.pdf:/home/liushuai/Zotero/storage/REWK7YS5/Summerfield - 2008 - Rapid GUI programming with Python and Qt the defi.pdf:application/pdf}
}

@article{robitaille_python_nodate,
	title = {Python {Qt} tutorial {Documentation}},
	language = {en},
	author = {Robitaille, Thomas P},
	pages = {18},
	file = {Robitaille - Python Qt tutorial Documentation.pdf:/home/liushuai/Zotero/storage/5GY4VZ5A/Robitaille - Python Qt tutorial Documentation.pdf:application/pdf}
}

@book{summerfield_rapid_2008-1,
	address = {Upper Saddle River, NJ},
	series = {Prentice {Hall} open source software development series},
	title = {Rapid {GUI} programming with {Python} and {Qt}: the definitive guide to {PyQt} programming},
	isbn = {978-0-13-235418-9},
	shorttitle = {Rapid {GUI} programming with {Python} and {Qt}},
	language = {en},
	publisher = {Prentice Hall},
	author = {Summerfield, Mark},
	year = {2008},
	note = {OCLC: ocn166273850},
	keywords = {Graphical user interfaces (Computer systems), Python (Computer program language), Qt (Electronic resource)},
	file = {Summerfield - 2008 - Rapid GUI programming with Python and Qt the defi.pdf:/home/liushuai/Zotero/storage/J3848E5B/Summerfield - 2008 - Rapid GUI programming with Python and Qt the defi.pdf:application/pdf}
}

@article{noauthor_tensorlayer_nodate,
	title = {{TensorLayer} {Documentation}},
	pages = {201},
	file = {TensorLayer Documentation.pdf:/home/liushuai/Zotero/storage/6HN294ZW/TensorLayer Documentation.pdf:application/pdf}
}

@article{dozat_incorporating_nodate,
	title = {Incorporating {Nesterov} {Momentum} into {Adam}},
	language = {en},
	author = {Dozat, Timothy},
	pages = {6},
	file = {Dozat - Incorporating Nesterov Momentum into Adam.pdf:/home/liushuai/Zotero/storage/JPMS9UJG/Dozat - Incorporating Nesterov Momentum into Adam.pdf:application/pdf}
}

@article{glorot_understanding_nodate,
	title = {Understanding the difﬁculty of training deep feedforward neural networks},
	abstract = {Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We ﬁrst observe the inﬂuence of the non-linear activations functions. We ﬁnd that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we ﬁnd that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We ﬁnd that a new non-linearity that saturates less can often be beneﬁcial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difﬁcult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.},
	language = {en},
	author = {Glorot, Xavier and Bengio, Yoshua},
	pages = {8},
	file = {Glorot 和 Bengio - Understanding the difﬁculty of training deep feedf.pdf:/home/liushuai/Zotero/storage/TMP2PYQP/Glorot 和 Bengio - Understanding the difﬁculty of training deep feedf.pdf:application/pdf}
}

@article{sutskever_importance_nodate,
	title = {On the importance of initialization and momentum in deep learning},
	abstract = {Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We ﬁnd that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned.},
	language = {en},
	author = {Sutskever, Ilya and Martens, James and Dahl, George and Hinton, Geoffrey},
	pages = {14},
	file = {Sutskever 等。 - On the importance of initialization and momentum i.pdf:/home/liushuai/Zotero/storage/33X7PH9H/Sutskever 等。 - On the importance of initialization and momentum i.pdf:application/pdf}
}

@book{graves_supervised_2012,
	address = {Berlin, Heidelberg},
	series = {Studies in {Computational} {Intelligence}},
	title = {Supervised {Sequence} {Labelling} with {Recurrent} {Neural} {Networks}},
	volume = {385},
	isbn = {978-3-642-24796-5 978-3-642-24797-2},
	url = {http://link.springer.com/10.1007/978-3-642-24797-2},
	language = {en},
	urldate = {2018-12-15},
	publisher = {Springer Berlin Heidelberg},
	author = {Graves, Alex},
	year = {2012},
	doi = {10.1007/978-3-642-24797-2},
	file = {Graves - 2012 - Supervised Sequence Labelling with Recurrent Neura.pdf:/home/liushuai/Zotero/storage/S9M6E33J/Graves - 2012 - Supervised Sequence Labelling with Recurrent Neura.pdf:application/pdf}
}

@article{srivastava_dropout:_nodate,
	title = {Dropout: {A} {Simple} {Way} to {Prevent} {Neural} {Networks} from {Overﬁtting}},
	abstract = {Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overﬁtting is a serious problem in such networks. Large networks are also slow to use, making it diﬃcult to deal with overﬁtting by combining the predictions of many diﬀerent large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of diﬀerent “thinned” networks. At test time, it is easy to approximate the eﬀect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This signiﬁcantly reduces overﬁtting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classiﬁcation and computational biology, obtaining state-of-the-art results on many benchmark data sets.},
	language = {en},
	author = {Srivastava, Nitish and Hinton, Geoﬀrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
	pages = {30},
	file = {Srivastava 等。 - Dropout A Simple Way to Prevent Neural Networks f.pdf:/home/liushuai/Zotero/storage/NJMZ4DYF/Srivastava 等。 - Dropout A Simple Way to Prevent Neural Networks f.pdf:application/pdf}
}

@article{lecun_efficient_1998,
	title = {Efficient {BackProp}},
	author = {Lecun, Yann and Bottou, Leon and Orr, Genevieve and Muller, Klausrobert},
	year = {1998},
	pages = {9--50}
}
